Supporting Management of Noncommunicable Diseases With Mobile Health (mHealth) Apps: Experimental Study

Abstract

Background Noncommunicable diseases (NCDs) are the leading global health problem in this century and are the principal causes of death and health care spending worldwide. Mobile health (mHealth) apps can help manage and prevent NCDs if people are willing to use them as supportive tools. Still, many people are reluctant to adopt these technologies. Implementing new apps could result in earlier intervention for many health conditions, preventing more serious complications. Objective This research project aimed to test the factors that facilitate the adoption of mHealth apps by users with NCDs. We focused on determining, first, what user interface (UI) qualities and complexity levels appeal to users in evaluating mHealth apps. We also wanted to determine whether people prefer that the data collected by an mHealth app be analyzed using a physician or an artificial intelligence (AI) algorithm. The contribution of this work is both theoretical and practical. We examined users’ considerations when adopting mHealth apps that promote healthy lifestyles and helped them manage their NCDs. Our results can also help direct mHealth app UI designers to focus on the most appealing aspects of our findings. Methods A total of 347 respondents volunteered to rate 3 models of mHealth apps based on 16 items that measured instrumentality, aesthetics, and symbolism. Respondents rated each model after reading 1 of 2 different scenarios. In one scenario, a physician analyzed the data, whereas, in the other, the data were analyzed by an AI algorithm. These scenarios tested the degree of trust people placed in AI algorithms versus the “human touch” of a human physician regarding analyzing data collected by an mHealth app. Results As shown by the responses, the involvement of a human physician in the application had a significant effect (P<.001) on the perceived instrumentality of the simple model. The complex model with more controls was rated significantly more aesthetic when associated with a physician performing data analysis rather than an AI algorithm (P=.03). Conclusions Generally, when participants found a human touch in the mHealth app (connection to a human physician who they assumed would analyze their data), they judged the app more favorably. Simple models were evaluated more positively than complex ones, and aesthetics and symbolism were salient predictors of preference. These trends suggest that designers and developers of mHealth apps should keep the designs simple and pay special attention to aesthetics and symbolic value.

Patient Perspectives on a Targeted Text Messaging Campaign to Encourage Screening for Diabetes: Qualitative Study

“…We assumed that patients would value this precision medicine approach. However, we found that participants were far more receptive to content when they perceived some sort of connection with their provider and had negative responses to messages that lacked this connection, which reflects other mHealth research [ 34 ]. This seems to reflect a tension that exists between patients’ awareness of the potential benefits of an automated process that identifies those who might benefit from screening and their desire to receive health care through a provider they know and trust.…”

Barriers and Facilitators to Trustworthy and Ethical AI-enabled Medical Care From Patient’s and Healthcare Provider’s Perspectives: A Literature Review

“…Among all the reviewed articles, 3 (2.1%) studied devices that were self-management software used directly by patients, 17–19 whereas the main users of the other devices discussed by 48 (33.1%) papers were healthcare providers. The remaining 94 (64.8%) papers did not specify the users.…”

“…Restricting the complexity of AI tools as well as providing clarity on how AI devices are regulated could facilitate patient trust. 17,19,57,91 It is also essential to provide patients with appropriate education about how to use AI tools and enhance their engagement in different phases of the design and implementation of AI technologies. 48,87,92,93…”

The link between construction apps’ acceptance and digital attributes of construction professionals: perspectives from digital competence model

“…Studies have additionally related factors such as proficient ability (Owoseni and Twinomurinzi, 2018), utilization of social media (Giunchiglia et al , 2018), innovative capacities (Huang and Ren, 2020), experience (Wang, 2020; Wang and Qi, 2021; Khrais and Alghamdi, 2021; Palos-Sanchez et al , 2021), environment (Torous et al , 2018), product design and research evidence (Baumel and Kane, 2018; Reinecke et al , 2021), willingness to learn (Cheung et al , 2021), experiential value (Kela et al , 2022), perceived value (Zolkepli et al , 2021), features and time spent on mobile phones (Shava and Chinyamurindi, 2018) and satisfaction and propensity (Alalwan, 2020) to apps acceptance. In some of these studies, younger people were found to be app users.…”

“…As indicated by the study’s findings, four technological functions – instruction provision, self-monitoring, self-regulation and goal attainment – indirectly affect continuance intention through perceived usefulness, and this indirect effect is moderated to such an extent by exercise self-efficacy that the association between perceived usefulness and continuance intention is more grounded for those with low self-efficacy. In an examination of the use of retailers’ apps, Kela et al (2022) discovered that perceived simplicity of use is a basic app quality that cultivates customers’ personal connections to apps. These linkages consequently influence their purchase channel preferences and true purchasing behaviour with the app, as well as their future intents to purchase and recommend the app to others.…”